{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sebastian Raschka   \n",
    "last updated: 05/07/2014  \n",
    "\n",
    "- [Link to this IPython Notebook on GitHub](https://github.com/rasbt/pattern_classification/blob/master/stat_pattern_class/supervised/parametric/parameter_estimation/max_likelihood_est_distributions.ipynb)   \n",
    "- [Link to the GitHub repository](https://github.com/rasbt/pattern_classification)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "I am really looking forward to your comments and suggestions to improve and extend this tutorial! Just send me a quick note   \n",
    "via Twitter: [&#64;rasbt](https://twitter.com/rasbt)  \n",
    "or Email: [bluewoodtree@gmail.com](mailto:bluewoodtree@gmail.com)\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to compute Maximum Likelihood Estimates (MLE) for different distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "<a name ='sections'></a>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#Sections\n",
    "\n",
    "- [Introduction](#introduction)\n",
    "- [General Concept](#general_concept)\n",
    "- [Multivariate Gaussian Distribution](#multi_gauss)\n",
    "- [Univariate Rayleigh Distribution](#uni_rayleigh)\n",
    "- [Univariate Poisson Distribution](#uni_poisson)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "<a name='introduction'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Maximum Likelihood Estimation (MLE) is a technique that uses the training data to estimate parameter values for a particular distribution. A popular example would be to estimate the mean and variance of a Normal distribution by computing it from the training data.\n",
    "\n",
    "MLE can be used on pattern classification tasks under the condition that the model of the distributions (and the number of parameters that we want to estimate) is known."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An introduction about how to use the Maximum Likelihood Estimate for pattern classification task can be found in an [earlier article](http://nbviewer.ipython.org/github/rasbt/pattern_classification/blob/master/parameter_estimation_techniques/maximum_likelihood_estimate.ipynb?create=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**To summarize the problem:** Using MLE, we want to estimate the values of the parameters for a given distribution. For example, in a pattern classification task with a Bayes classifier and normal distributed class-conditional densities, those parameters would be the *mean* and *variance* (  $p(\\pmb x \\; | \\; \\omega_i) \\sim N(\\pmb\\mu, \\pmb\\sigma^2)$ ). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<a name='general_concept'></a>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#General Concept"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the Maximum Likelihood Estimate (MLE), we assume that we have a data set of *i.i.d.* (independent and identically distributed) samples    \n",
    "$D = \\left\\{ \\pmb x_1, \\pmb x_2,..., \\pmb x_n \\right\\} $."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Likelihood"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The probability of observing the data set $D = \\left\\{ \\pmb x_1, \\pmb x_2,..., \\pmb x_n \\right\\} $ can be pictured as probability to observe a particular sequence of patterns,  \n",
    "where the probability of observing a particular patterns depends on $\\pmb \\theta$, the parameters the underlying (class-conditional) distribution. In order to apply MLE, we have to make the assumption that the samples are *i.i.d.* (independent and identically distributed).\n",
    "<br>\n",
    "<br>\n",
    "$p(D\\; | \\;  \\pmb \\theta\\;) \\\\\n",
    "= p(\\pmb x_1 \\; | \\; \\pmb \\theta\\;)\\; \\cdot \\; p(\\pmb x_2 \\; | \\;\\pmb \\theta\\;) \\; \\cdot \\;...  \\; p(\\pmb x_n \\; | \\; \\pmb \\theta\\;) \\\\ \n",
    "= \\prod_{k=1}^{n} \\; p(\\pmb x_k \\pmb \\; | \\; \\pmb \\theta \\;)$  \n",
    "<br>\n",
    "Where $\\pmb\\theta$ is the parameter vector, that contains the parameters for a particular distribution that we want to estimate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and $p(D\\; | \\;  \\pmb  \\theta\\;)$ is also called the ***likelihood of $\\pmb\\ \\theta$***."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, we take the natural logarithm in order to compute the so-called ***log-likelihood***:  \n",
    "\n",
    "\n",
    "\n",
    "$p(D|\\theta) = \\prod_{k=1}^{n} p(x_k|\\theta) \\\\\n",
    "\\Rightarrow l(\\theta) = \\sum_{k=1}^{n} ln \\; p(x_k|\\theta)$    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Goal:\n",
    "Compute $\\hat{\\pmb \\theta}$, which are the values that maximize $p(D\\; | \\;  \\pmb \\theta\\;)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In pattern classification tasks we have multiple classes $\\omega_j$ with independent class-conditional densities $p(\\pmb x | \\omega_j)$, which are dependent on the parameters of the distribution $p(\\pmb x | \\omega_j, \\pmb \\theta_j)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Approach:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to maximize $p(D\\; | \\;  \\pmb \\theta\\;)$, we can apply the rules of differential calculus for every parameters to the ***log-likelihoods***:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\nabla_{\\pmb \\theta} \\equiv \\begin{bmatrix}  \n",
    "\\frac{\\partial \\; }{\\partial \\; \\theta_1} \\\\\n",
    "\\frac{\\partial \\; }{\\partial \\; \\theta_2} \\\\\n",
    "...\\\\\n",
    "\\frac{\\partial \\; }{\\partial \\; \\theta_p}\\end{bmatrix}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Which as to be done for every class $\\omega_j$ separately, and for our convenience, let us drop the class labels *j* for now, so that for a class $\\omega_j$:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\nabla_{\\pmb \\theta} l(\\pmb\\theta) \\equiv \\begin{bmatrix}  \n",
    "\\frac{\\partial \\; L(\\pmb\\theta)}{\\partial \\; \\theta_1} \\\\\n",
    "\\frac{\\partial \\; L(\\pmb\\theta)}{\\partial \\; \\theta_2} \\\\\n",
    "...\\\\\n",
    "\\frac{\\partial \\; L(\\pmb\\theta)}{\\partial \\; \\theta_p}\\end{bmatrix}$\n",
    "$= \\begin{bmatrix}  \n",
    "0 \\\\\n",
    "0 \\\\\n",
    "...\\\\\n",
    "0\\end{bmatrix}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='multi_gauss'></a>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Multivariate Gaussian Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Probability Density Function:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "$p(\\pmb x) \\sim N(\\pmb \\mu|\\Sigma)$\n",
    "\n",
    "$p(\\pmb x) \\sim \\frac{1}{(2\\pi)^{d/2} \\; |\\Sigma|^{1/2}} exp \\bigg[ -\\frac{1}{2}(\\pmb x - \\pmb \\mu)^t \\Sigma^{-1}(\\pmb x - \\pmb \\mu) \\bigg]$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**likelihood of $\\pmb\\ \\theta$:**  \n",
    "\n",
    "$\\Rightarrow p(D\\; | \\;  \\pmb \\theta\\;) = \\prod_{k=1}^{n} \\; p(\\pmb x_k \\pmb \\; | \\; \\pmb \\theta \\;)\\\\\n",
    "\\Rightarrow p(D\\; | \\;  \\pmb \\theta\\;) =  \\prod_{k=1}^{n} \\; \\frac{1}{(2\\pi)^{d/2} \\; |\\Sigma|^{1/2}} exp \\bigg[ -\\frac{1}{2}(\\pmb x - \\pmb \\mu)^t \\Sigma^{-1}(\\pmb x - \\pmb \\mu) \\bigg]$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "####log-likelihood of $\\pmb\\ \\theta$ (natural logarithm):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$l(\\pmb\\theta) =  \\sum\\limits_{k=1}^{n} - \\frac{1}{2}(\\pmb x - \\pmb \\mu)^t \\pmb \\Sigma^{-1} \\; (\\pmb x - \\pmb \\mu) - \\frac{d}{2} \\; ln \\; 2\\pi - \\frac{1}{2} \\;ln \\; |\\pmb\\Sigma|$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The 2 parameters that we want to estimate are $\\pmb \\mu_i$ and $\\pmb \\Sigma_i$, are   \n",
    "\n",
    "\n",
    "$\\pmb \\theta_i = \\bigg[ \\begin{array}{c}\n",
    "\\ \\theta_{i1} \\\\\n",
    "\\ \\theta_{i2} \\\\\n",
    "\\end{array} \\bigg]=\n",
    "\\bigg[ \\begin{array}{c}\n",
    "\\pmb \\mu_i \\\\\n",
    "\\pmb \\Sigma_i \\\\\n",
    "\\end{array} \\bigg]$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Maximum Likelihood Estimate (MLE):"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to obtain the MLE $\\boldsymbol{\\hat{\\theta}}$, we maximize $l (\\pmb  \\theta)$, which can be done via differentiation:\n",
    "\n",
    "with \n",
    "$\\nabla_{\\pmb \\theta} \\equiv \\begin{bmatrix}  \n",
    "\\frac{\\partial \\; }{\\partial \\; \\theta_1} \\\\ \n",
    "\\frac{\\partial \\; }{\\partial \\; \\theta_2}\n",
    "\\end{bmatrix} = \\begin{bmatrix} \n",
    "\\frac{\\partial \\; }{\\partial \\; \\pmb \\mu} \\\\ \n",
    "\\frac{\\partial \\; }{\\partial \\; \\pmb \\sigma}\n",
    "\\end{bmatrix}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\Rightarrow \\nabla_{\\pmb \\theta} l(\\pmb\\theta) = \\sum\\limits_{k=1}^n \\nabla_{\\pmb \\theta} \\;ln\\; p(\\pmb x| \\pmb \\theta) = 0 $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1st parameter $\\theta_1 = \\pmb \\mu$\n",
    "\n",
    "${\\hat{\\pmb\\mu}} = \\frac{1}{n} \\sum\\limits_{k=1}^{n} \\pmb x_k$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2nd parameter $\\theta_2 = \\Sigma$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "${\\hat{\\pmb\\Sigma}} = \\frac{1}{n} \\sum\\limits_{k=1}^{n} (\\pmb x_k - \\hat{\\mu})(\\pmb x_k - \\hat{\\mu})^t$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code for multivariate Gaussian MLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# loading packages\n",
    "\n",
    "%pylab inline\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def mle_gauss_mu(samples):\n",
    "    \"\"\"\n",
    "    Calculates the Maximum Likelihood Estimate for a mean vector\n",
    "    from a multivariate Gaussian distribution.\n",
    "    \n",
    "    Keyword arguments:\n",
    "        samples (numpy array): Training samples for the MLE.\n",
    "            Every sample point represents a row; dimensions by column.\n",
    "            \n",
    "    Returns a row vector (numpy.array) as the MLE mean estimate.\n",
    "    \n",
    "    \"\"\"\n",
    "    dimensions = samples.shape[1]\n",
    "    mu_est = np.zeros((dimensions,1))\n",
    "    for dim in range(dimensions):\n",
    "        mu_est = np.zeros((dimensions,1))\n",
    "        col_mean = sum(samples[:,dim])/len(samples[:,dim])\n",
    "        mu_est[dim] = col_mean\n",
    "    return mu_est"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def mle_gausscov(samples, mu_est):\n",
    "    \"\"\"\n",
    "    Calculates the Maximum Likelihood Estimate for the covariance matrix.\n",
    "    \n",
    "    Keyword Arguments:\n",
    "        x_samples: np.array of the samples for 1 class, n x d dimensional \n",
    "        mu_est: np.array of the mean MLE, d x 1 dimensional\n",
    "        \n",
    "    Returns the MLE for the covariance matrix as d x d numpy array.\n",
    "    \n",
    "    \"\"\"\n",
    "    dimensions = samples.shape[1]\n",
    "    assert (dimensions == mu_est.shape[0]), \"columns of sample set and rows of'\\\n",
    "                'mu vector (i.e., dimensions) must be equal.\"\n",
    "    cov_est = np.zeros((dimensions,dimensions))\n",
    "    for x_vec in samples:\n",
    "        x_vec = x_vec.reshape(dimensions,1)\n",
    "        cov_est += (x_vec - mu_est).dot((x_vec - mu_est).T)\n",
    "    return cov_est / len(samples)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample training data for MLE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\pmb \\mu = \\Bigg[ \\begin{array}{c}\n",
    "\\ 0 \\\\\n",
    "\\ 0\n",
    "\\end{array} \\Bigg]\\;, \\quad \\quad \n",
    "\\pmb \\Sigma = \\Bigg[ \\begin{array}{ccc}\n",
    "\\ 1 & 0 & 0 \\\\\n",
    "\\ 0 & 1 & 0\n",
    "\\end{array} \\Bigg] \\quad$\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dimensions: 100x2\n"
     ]
    }
   ],
   "source": [
    "# true parameters and 100 3D training data points\n",
    "\n",
    "mu_vec = np.array([[0],[0]])\n",
    "cov_mat = np.eye(2)\n",
    "\n",
    "multi_gauss = np.random.multivariate_normal(mu_vec.ravel(), cov_mat, 100)\n",
    "print('Dimensions: {}x{}'.format(multi_gauss.shape[0], multi_gauss.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Estimate parameters via MLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+----+------------+-----------------+\n",
      "| mu | true_param |    MLE_param    |\n",
      "+----+------------+-----------------+\n",
      "|    |    [[0]    |  [[ 0.        ] |\n",
      "|    |    [0]]    |  [-0.04300124]] |\n",
      "+----+------------+-----------------+\n",
      "+------------+-------------+-----------------------------+\n",
      "| covariance |  true_param |          MLE_param          |\n",
      "+------------+-------------+-----------------------------+\n",
      "|            |  [[ 1.  0.] |  [[ 1.29511268  0.1386421 ] |\n",
      "|            |  [ 0.  1.]] |  [ 0.1386421   0.72096049]] |\n",
      "+------------+-------------+-----------------------------+\n"
     ]
    }
   ],
   "source": [
    "import prettytable\n",
    "\n",
    "# mean estimate\n",
    "mu_mle = mle_gauss_mu(multi_gauss)\n",
    "mu_mle_comp = prettytable.PrettyTable([\"mu\", \"true_param\", \"MLE_param\"])\n",
    "mu_mle_comp.add_row([\"\",mu_vec, mu_mle])\n",
    "print(mu_mle_comp)\n",
    "\n",
    "# covariance estimate\n",
    "cov_mle = mle_gausscov(multi_gauss, mu_mle)\n",
    "mle_gausscov_comp = prettytable.PrettyTable([\"covariance\", \"true_param\", \"MLE_param\"])\n",
    "mle_gausscov_comp.add_row([\"\",cov_mat, cov_mle])\n",
    "print(mle_gausscov_comp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "### Implementing the Multivariate Gaussian Density Function\n",
    "\n",
    "def pdf_multivariate_gauss(x, mu, cov):\n",
    "    \"\"\"\n",
    "    Caculate the multivariate normal density (pdf)\n",
    "\n",
    "    Keyword arguments:\n",
    "        x = numpy array of a \"d x 1\" sample vector\n",
    "        mu = numpy array of a \"d x 1\" mean vector\n",
    "        cov = \"numpy array of a d x d\" covariance matrix\n",
    "        \n",
    "    \"\"\"\n",
    "    assert(mu.shape[0] > mu.shape[1]), 'mu must be a row vector'\n",
    "    assert(x.shape[0] > x.shape[1]), 'x must be a row vector'\n",
    "    assert(cov.shape[0] == cov.shape[1]), 'covariance matrix must be square'\n",
    "    assert(mu.shape[0] == cov.shape[0]), 'cov_mat and mu_vec must have the same dimensions'\n",
    "    assert(mu.shape[0] == x.shape[0]), 'mu and x must have the same dimensions'\n",
    "    part1 = 1 / ( ((2* np.pi)**(len(mu)/2)) * (np.linalg.det(cov)**(1/2)) )\n",
    "    part2 = (-1/2) * ((x-mu).T.dot(np.linalg.inv(cov))).dot((x-mu))\n",
    "    return float(part1 * np.exp(part2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 100)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Z_true.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAgQAAAIECAYAAABmAjaWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105b89cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Probability Density Function\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig = plt.figure(figsize=(9, 9))\n",
    "ax = fig.gca(projection='3d')\n",
    "\n",
    "X = np.linspace(-5, 5, 100)\n",
    "Y = np.linspace(-5, 5, 100)\n",
    "X,Y = np.meshgrid(X,Y)\n",
    "\n",
    "Z_mle = []\n",
    "for i,j in zip(X.ravel(),Y.ravel()):\n",
    "    Z_mle.append(pdf_multivariate_gauss(np.array([[i],[j]]), mu_mle, cov_mle))\n",
    "Z_mle = np.asarray(Z_mle).reshape(len(Z_mle)**0.5, len(Z_mle)**0.5)   \n",
    "surf = ax.plot_wireframe(X, Y, Z_mle, color='red', rstride=2, cstride=2, alpha=0.3, label='MLE')\n",
    "\n",
    "Z_true = []\n",
    "for i,j in zip(X.ravel(),Y.ravel()):\n",
    "    Z_true.append(pdf_multivariate_gauss(np.array([[i],[j]]), mu_vec, cov_mat))\n",
    "Z_true = np.asarray(Z_true).reshape(len(Z_true)**0.5, len(Z_true)**0.5)\n",
    "surf = ax.plot_wireframe(X, Y, Z_true, color='green', rstride=2, cstride=2, alpha=0.3, label='true param.')\n",
    "\n",
    "ax.set_zlim(0, 0.2)\n",
    "ax.zaxis.set_major_locator(plt.LinearLocator(10))\n",
    "ax.zaxis.set_major_formatter(plt.FormatStrFormatter('%.02f'))\n",
    "ax.set_xlabel('X')\n",
    "ax.set_ylabel('Y')\n",
    "ax.set_zlabel('p(x)')\n",
    "ax.legend()\n",
    "\n",
    "plt.title('True vs. Predicted Gaussian densities')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='uni_rayleigh'></a>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Univariate Rayleigh Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probability Density Function\n",
    "\n",
    "$p(x|\\theta) =  \\Bigg\\{ \\begin{array}{c}\n",
    "  2\\theta xe^{- \\theta x^2},\\quad \\quad x \\geq0, \\\\\n",
    "  0,\\quad otherwise. \\\\\n",
    "  \\end{array}$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Derive a formula for the maximum likelihood estimate of $\\theta$ , i.e., $\\hat{{\\theta}}_{mle}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$p(D|\\theta) = \\prod_{k=1}^{n} p(x_k|\\theta) $\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\prod_{k=1}^{n}  2 \\theta x_ke^{- \\theta x_{k}^{2}}  $"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Taking the natural logarithm to get the log-likelihood:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\Rightarrow L(\\theta) = \\sum_{k=1}^{n} ln \\; p(x_k|\\theta)$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\sum_{k=1}^{n} ln \\bigg( 2 \\theta x_ke^{- \\theta x_{k}^{2}} \\bigg) \\\\ \n",
    "= \\sum_{k=1}^{n} ln (2 \\theta x_k) - ( \\theta x_{k}^{2})$\n",
    "<br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Differentiating the log-likelihood:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "$\\Rightarrow \\frac{\\partial L}{\\partial (\\theta)} = \\frac{\\partial}{\\partial (\\theta)} \\sum_{k=1}^{n} ln (2 \\theta x_k) - ( \\theta x_{k}^{2})$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "$= \\sum_{k=1}^{n}  \\frac{\\partial}{\\partial (\\theta)}ln (2 \\theta x_k) - ( \\theta x_{k}^{2})\\\\\n",
    "= \\sum_{k=1}^{n}  \\frac{2x_k}{2\\theta x_k} - x_{k}^{2} \\\\\n",
    "= \\sum_{k=1}^{n} \\frac{1}{\\theta} - x_{k}^{2}$\n",
    "\n",
    "#### Getting the maximum for $p(D|\\theta)$\n",
    "\n",
    "$\\Rightarrow \\sum_{k=1}^{n} \\frac{1}{\\theta} - x_{k}^{2} = 0 \\\\\n",
    "\\sum_{k=1}^{n} \\frac{1}{\\theta} = \\sum_{k=1}^{n} x_{k}^{2}$\n",
    "\n",
    "$\\frac{n}{\\theta} = \\sum_{k=1}^{n} x_{k}^{2} \\\\\n",
    "\\frac{\\theta}{n} = \\frac{1}{\\sum_{k=1}^{n} x_{k}^{2}} \\\\\n",
    "\\theta = \\frac{n}{\\sum_{k=1}^{n} x_{k}^{2}}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "### Code for univariate Rayleigh MLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# loading packages\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def comp_theta_mle(d):\n",
    "    \"\"\"\n",
    "    Computes the Maximum Likelihood Estimate for a given 1D training\n",
    "    dataset for a Rayleigh distribution.\n",
    "    \n",
    "    \"\"\"\n",
    "    theta = len(d) / sum([x**2  for x in d])\n",
    "    return theta    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def likelihood_ray(x, theta):\n",
    "    \"\"\"\n",
    "    Computes the class-conditional probability for an univariate\n",
    "    Rayleigh distribution\n",
    "    \n",
    "    \"\"\"\n",
    "    return 2*theta*x*np.exp(-theta*(x**2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Theta MLE: 0.00123877361412\n"
     ]
    }
   ],
   "source": [
    "training_data = [12, 17, 20, 24, 25, 30, 32, 50]\n",
    "\n",
    "theta = comp_theta_mle(training_data)\n",
    "\n",
    "print(\"Theta MLE:\", theta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAnQAAAH4CAYAAADD83DxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106371b10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Probability Density Function\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "x_range = np.arange(0, 150, 0.1)\n",
    "y_range = [likelihood_ray(x, theta) for x in x_range]\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(x_range, y_range, lw=2)\n",
    "plt.title('Probability density function for the Rayleigh distribution')\n",
    "plt.ylabel('p(x|theta)')\n",
    "\n",
    "ftext = 'theta = {:.5f}'.format(theta)\n",
    "plt.figtext(.15,.8, ftext, fontsize=11, ha='left')\n",
    "\n",
    "\n",
    "plt.ylim([0,0.04])\n",
    "plt.xlim([0,120])\n",
    "plt.xlabel('random variable x')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name='uni_poisson'></a>\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Univariate Poisson Distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probability Density Function\n",
    "\n",
    "$p(x|\\theta) = \\frac{e^{-\\theta}\\theta^{xk}}{x_k!}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Derive a formula for the maximum likelihood estimate of $\\theta$ , i.e., $\\hat{{\\theta}}_{mle}$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$p(D|\\theta) = \\prod_{k=1}^{n} p(x_k|\\theta)$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\prod_{k=1}^{n}\n",
    "\\frac{e^{-\\theta}\\theta^{xk}}{x_k!}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Taking the natural logarithm to get the log-likelihood:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$p(D|\\theta) = L(\\theta) = \\prod_{k=1}^{n} p(x_k|\\theta)$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\sum_{k=1}^{n} ln \\bigg( \\frac{e^{-\\theta}\\theta^{xk}}{x_k!} \\bigg)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\sum_{k=1}^{n} ln(e^{-\\theta}\\theta^{xk}) - ln({x_k!})$ (simplify by removing the scalar, which becomes 0 in the derivative)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\sum_{k=1}^{n} ln(e^{-\\theta}\\theta^{xk})$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\sum_{k=1}^{n} ln(e^{-\\theta}) + ln(\\theta^{xk})$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\sum_{k=1}^{n} -\\theta + x_k \\; ln(\\theta)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Differentiating the log-likelihood:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\frac{\\partial \\; L(\\theta)}{\\partial \\; \\theta} = \\frac{\\partial \\; }{\\partial \\; \\theta} \\bigg( \\sum_{k=1}^{n} -\\theta + x_k \\; ln(\\theta)\\bigg)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\sum_{k=1}^{n} \\frac{\\partial \\; }{\\partial \\; \\theta} \\bigg(  -\\theta + x_k \\; ln(\\theta)\\bigg)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$= \\sum_{k=1}^{n} \\bigg( -1 + \\frac{x_k}{\\theta} \\bigg)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Getting the maximum for $p(D|\\theta)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\Rightarrow -n +  \\sum_{k=1}^{n} x_k \\; \\cdot \\frac{1}{\\theta} = 0$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$\\theta = \\frac{\\sum_{k=1}^{n} x_k }{n}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "<br>\n",
    "### Code for univariate Poisson MLE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def poisson_theta_mle(d):\n",
    "    \"\"\"\n",
    "    Computes the Maximum Likelihood Estimate for a given 1D training\n",
    "    dataset from a Poisson distribution.\n",
    "    \n",
    "    \"\"\"\n",
    "    return sum(d) / len(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "def likelihood_poisson(x, lam):\n",
    "    \"\"\"\n",
    "    Computes the class-conditional probability for an univariate\n",
    "    Poisson distribution\n",
    "    \n",
    "    \"\"\"\n",
    "    if x // 1 != x:\n",
    "        likelihood = 0\n",
    "    else:\n",
    "        likelihood = math.e**(-lam) * lam**(x) / math.factorial(x)\n",
    "    return likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Drawing training data\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "true_param = 1.0\n",
    "poisson_data = np.random.poisson(lam=true_param, size=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MLE: 1.05\n"
     ]
    }
   ],
   "source": [
    "mle_poiss = poisson_theta_mle(poisson_data)\n",
    "\n",
    "print('MLE:', mle_poiss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAmcAAAH4CAYAAAAPakoaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106fbc748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Probability Density Function\n",
    "from matplotlib import pyplot as plt\n",
    "    \n",
    "x_range = np.arange(0, 5, 0.1)\n",
    "y_true = [likelihood_poisson(x, true_param) for x in x_range]\n",
    "y_mle = [likelihood_poisson(x, mle_poiss) for x in x_range]\n",
    "\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(x_range, y_true, lw=2, alpha=0.5, linestyle='--', label='true parameter ($\\lambda={}$)'.format(true_param))\n",
    "plt.plot(x_range, y_mle, lw=2, alpha=0.5, label='MLE ($\\lambda={}$)'.format(mle_poiss))\n",
    "plt.title('Poisson probability density function for the true and estimated parameters')\n",
    "plt.ylabel('p(x|theta)')\n",
    "plt.xlim([-1,5])\n",
    "plt.xlabel('random variable x')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
